Concentrations Of Environmental Pollutants Research Articles

Ag nanoparticle functionalized vertical WS2 nanoflakes as SERS substrate for chemical sensing

Two-dimensional transition metal dichalcogenides (TMDCs) present a unique opportunity as Surface Enhanced Raman Scattering (SERS) substrates due to their superior optical properties. Here, vertically oriented tungsten disulfide (WS₂) flakes were synthesized on a silicon (Si) substrate using the pulsed laser deposition (PLD) technique. The WS₂ flakes were employed as SERS substrates for detecting low concentrations of environmental pollutants, like Rhodamine B (RhB) and Methyl Orange (MO) dyes, achieving promising enhancement factors of approximately 10⁷ with a 532 nm excitation laser. To further enhance the plasmonic activity of the vertical WS₂ flakes, silver nanoparticles (NPs) were decorated on their surface via thermal evaporation. The Ag NP-functionalized WS₂ flakes exhibited superior detection capabilities compared to pristine WS₂ flakes, achieving remarkable sensitivity at ultralow concentrations of 10⁻¹⁶ M for RhB and 10–17 M for MO dye, with enhancement factors of the order of 10⁸. The SERS performance of the WS₂-Ag substrate was also evaluated using a 633 nm laser, successfully detecting Methylene Blue (MB) dye at an ultralow concentration of 10⁻¹³ M. Prolonged exposure to RhB, MO, and MB dyes can cause cancer, neurotoxicity, and respiratory irritation. Thus, sensitive detection of these dyes at ultralow concentrations is highly crucial. A charge transfer mechanism between WS₂ and RhB dye molecules is proposed, along with the contribution of Ag NPs, to explain the enhanced detection capabilities of the nanocomposite SERS substrate.

A Review : Air Pollution Prediction using Machine Learning Techniques

Air pollution poses a critical threat to public health and the environment, necessitating accurate prediction methods for effective mitigation strategies. This paper explores the application of machine learning techniques in predicting air pollution levels, aiming to improve forecasting accuracy and enable proactive interventions. By leveraging diverse data sources, including satellite imagery, weather forecasts, and socioeconomic factors, machine learning models can capture complex relationships between environmental variables and pollutant concentrations. Regression models, neural networks, and ensemble methods are investigated for their effectiveness in air pollution prediction, considering factors such as feature selection, model evaluation metrics, and real-time data integration. Case studies highlight successful applications of machine learning in air quality prediction, demonstrating the potential for scalable and accessible monitoring systems. The findings underscore the importance of continued research in this field to address emerging challenges and advance environmental management practices. By harnessing the power of data-driven insights, we can create healthier and more sustainable communities for current and future generations.

Causal Associations of Environmental Pollution and Cardiovascular Disease: A Two-Sample Mendelian Randomization Study.

There is growing evidence that concentrations of environmental pollutants are previously associated with cardiovascular disease; however, it is unclear whether this association reflects a causal relationship. We utilized a two-sample Mendelian randomization (MR) approach to investigate how environmental pollution affects the likelihood of developing cardiovascular disease. We primarily employed the inverse variance weighted (IVW) method. Additionally, to ensure the robustness of our findings, we conducted several sensitivity analyses using alternative methodologies. These included maximum likelihood, MR-Egger regression, weighted median method and weighted model methods. Inverse variance weighted estimates suggested that an SD increase in PM2.5 exposure increased the risk of heart failure (OR = 1.40, 95% CI 1.02-1.93, p = 0.0386). We found that an SD increase in PM10 exposure increased the risk of hypertension (OR = 1.45, 95% CI 1.02-2.05, p = 0.03598) and atrial fibrillation (OR = 1.41, 95% CI 1.03-1.94, p = 0.03461). Exposure to chemical or other fumes in a workplace was found to increase the risk of hypertension (OR = 3.08, 95% CI 1.40-6.78, p = 0.005218), coronary artery disease (OR = 1.81, 95% CI 1.00-3.26, p = 0.04861), coronary heart disease (OR = 3.15, 95% CI 1.21-8.16, p = 0.0183) and myocardial infarction (OR = 3.03, 95% CI 1.13-8.17, p = 0.02802). This study reveals the causal relationship between air pollutants and cardiovascular diseases, providing new insights into the protection of cardiovascular diseases.

Ash Properties and Environmental Impact of Coal and Its Blend with Patent Fuel for Climate Sustainability

Fossil fuels are still widely used today, and exploring more sustainable ways of using coal is crucial. One promising approach is to develop a patented fuel with reduced harmful gas emissions during combustion. This study aims to investigate the properties of the ash produced by burning steam coal mixed with various ratios of patent fuel. The combustion process was carried out using a standard solid fuel boiler with a power output of 70 kW. The ash samples were analyzed using various analytical methods, and ash leaching tests were conducted. The study found that adding patent fuel to coal affects combustion and ash composition. Determining the thermal stability of ash samples showed that adding patent fuel to coal results in decomposition starting earlier and in stronger combustion. The ash produced by patent fuel–coal mixtures contains lower concentrations of Ba, Cr, Ga, Li, Mn, V, and Zn than pure coal combustion. Leaching tests showed that coal ash leachates had higher concentrations of environmental pollutants, such as As, Cd, Co, Mn, Mo, Sb, and U, than patent fuel leachates. Adding patent fuel to coal affects combustion, ash properties, emissions, and disposal. Understanding these implications can help to develop sustainable coal usage and reduce environmental impact.

Relationship Between Air Pollutants and the Incidence of Epistaxis in Yangzhou.

Objectives: This project aims to explore the relationship between the air quality index (AQI), the concentration of 6 air pollutants, and the incidence of epistaxis in Yangzhou. Also, to provide reference information for the prevention and treatment of epistaxis. Methods: Data of patients with epistaxis admitted to the Northern Jiangsu People's Hospital Affiliated to Yangzhou University from January 2017 to December 2021 were collected. In addition, the local AQI and the concentrations of 6 air pollutants, namely particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), were analyzed at the time of onset. Furthermore, the correlation with the incidence of epistaxis has been analyzed. Results: From 2017 to 2021, there were 24,721 patients with epistaxis aged from 0 to 17 years old while male patients were more than females. The incidence was higher in April, May, and June. There was a statistically significant difference in the number of daily epistaxis in different months and under AQI conditions (P < .05). Spearman's correlation analysis showed that there was a positive correlation between the number of daily epistaxis and the concentrations of AQI, CO, NO2, O3, PM2.5, PM10, and SO2 in Yangzhou, in which O3, PM10, and SO2 were highly correlated with the average number of daily epistaxis, and there was no obvious time lag effect of air pollutants on epistaxis. Conclusion: Epistaxis in the Yangzhou area is more common in males, mostly occurs in 0 to 17 years old, with seasonal. There was also a positive correlation between the incidence of epistaxis and air pollutants in Yangzhou. Therefore, by reducing the AQI index in daily life, and reducing the concentration of environmental pollutants in the air, the occurrence of epistaxis could be prevented and reduced to a certain extent.

Analyzing heavy metal contamination for one of the high-rate consumption fruits in Iran: A probabilistic health risk assessment

Good health and well-being is one of the sustainable development goals (SDGs) that can be achieved through fruit consumption. This study measured cucumber (Cucumis sativus L.) heavy metal concentrations. Inductively coupled plasma-mass spectrometry (ICP-OES) was used to analyze the samples for heavy metal content. The uncertainty and sensitivity analyses of carcinogenic and non-carcinogenic heavy metal intake via cucumber (Cucumis sativus L.) consumption were assessed by Monte Carlo simulation. The mean ± SD levels of Cu, Pb, Zn, Cd, and As were determined to be 157.87 ± 128.54, 33.81 ± 6.27, 288.46 ± 114.59, 35.22 ± 18.67, and 33.6 ± 18.1 μg/kg, respectively. The 95th percentile of HI related to heavy metal intake via cucumber (Cucumis sativus L.) among children and adults were 2.64 and 1.75, respectively. Also, the 95th percentile of ELCR related to heavy metal were 8.26E-4 and 4.14E-3 among children and adults, respectively. The 95th percentile of LTCR of As among adults and As, Cd, and Pb among children were in the WHO target range (1E-04 to 1E-06) so reducing the concentration of them can help to reduce overall LTCR. When HQ and LTCR are below the cut limits, reducing heavy metals in high-consumption meals is a good way to lower them. In general, due to the wide consumption of various fruits, such as cucumber (Cucumis sativus L.), the concentration of environmental pollutants in their edible tissues should be monitored regularly, and the concentration of pollutants in these tissues should be minimized by proper planning.

Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is the top priority in the construction of smart cities. The purpose of this study is to propose a method for identifying toxicological biological indicators of human exposure in smart cities based on public health data and deep learning to achieve accurate assessment and management of exposure risks. Initially, the study used a network of sensors within the smart city infrastructure to collect environmental monitoring data, including indicators such as air quality, water quality, and soil pollution. Using public health data, a database containing information on types and concentrations of environmental pollutants has been established. Convolutional neural network was used to recognize the pattern of environmental monitoring data, identify the relationship between different indicators, and build the correlation model between health indicators and environmental indicators. Identify biological indicators associated with environmental pollution exposure through training optimization. Experimental analysis showed that the prediction accuracy of the model reached 93.45%, which could provide decision support for the government and the health sector. In the recognition of the association pattern between respiratory diseases, cardiovascular diseases and environmental exposure factors such as PM2.5 and SO2, the fitting degree between the model and the simulation value reached more than 0.90. The research design model can play a positive role in public health and provide new decision-making ideas for protecting public health.

Association between ambient air pollutants and short-term mortality risks during 2015-2019 in Guangzhou, China.

With the development of technology and industry, the problem of global air pollution has become difficult to ignore. We investigated the association between air pollutant concentrations and daily all-cause mortality and stratified the analysis by sex, age, and season. Data for six air pollutants [fine particulate matter (PM2.5), inhalable particles (PM10), nitric dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO)] and daily mortality rates were collected from 2015 to 2019 in Guangzhou, China. A time-series study using a quasi-Poisson generalized additive model was used to examine the relationships between environmental pollutant concentrations and mortality. Mortality data for 296,939 individuals were included in the analysis. The results showed that an increase of 10 μg/m3 in the concentrations of PM2.5, PM10, SO2, O3, NO2, and CO corresponded to 0.84% [95% confidence interval (CI): 0.47, 1.21%], 0.70% (0.44, 0.96%), 3.59% (1.77, 5.43%), 0.21% (0.05, 0.36%), 1.06% (0.70, 1.41%), and 0.05% (0.02, 0.09%), respectively. The effects of the six air pollutants were more significant for male individuals than female individuals, the cool season than the warm season, and people 75 years or older than those younger than 75 years. PM2.5, PM10, SO2, and NO2 were all associated with neoplasms and circulatory and respiratory diseases. The two-pollutant models found that PM2.5, PM10, and NO2 may independently affect the risk of mortality. The results showed that exposure to PM2.5, PM10 and NO2 may increase the risk of daily all-cause excessive mortality in Guangzhou.

Environmental pollutants PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) impair human cognitive functions.

Environmental pollution is an emerging global public health problem across the world and causes serious threats to ecosystems, human health, and the planet. This study is designed to explore the impact of environmental pollution particulate matter PM2.5, PM10, carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) on cognitive functions in students from schools located in or away from air-polluted areas. In this study, two schools were selected: one was located near a traffic-polluted area (school #1), and the second was in an area away from the traffic-polluted area (school #2). In this study, a total of 300 students were recruited: 150 (75 male and 75 female) students from school #1 located in a traffic-polluted area, and 150 students (75 male and 75 female) from school #2 located away from a traffic polluted area. The overall average age of students was 13.53±1.20 years. The students were selected based on age, gender, health status, height, weight, BMI, ethnicity, and homogenous socio-economic and educational status. The pollutants PM2.5, PM10, CO, NO2, O3, and SO2 were recorded in the surrounding environment. The overall mean concentration of environmental pollutants in school #1 was 35.00±0.65 and in school #2 was 29.95±0.32. The levels of airborne particles were measured, and the cognitive functions were recorded using the Cambridge Neuropsychological Test Automated Battery (CANTAB). The students performed the cognitive functions tasks, including the attention switching task (AST), choice reaction time (CRT), and motor screening task (MOT). The results revealed that the AST-Mean 928.34±182.23 vs. 483.79±146.73 (p=0.001), AST-mean congruent 889.12±197.12 vs. 473.30±120.11 (p=0.001), AST-mean in-congruent 988.98±201.27 vs. 483.87±144.57 (p=0.001), CRT-Mean 721.36±251.72 vs. 418.17±89.71 (p=0.001), and MOT-Mean 995.07±394.37 vs. 526.03±57.83 (p=0.001) were significantly delayed among the students who studied in school located in the traffic polluted area compared to students who studied in school which was located away from the traffic-polluted area. Environmental pollution was significantly higher in motor vehicle-congested areas. Cognitive functions were impaired among the students who were studying in a school located in a polluted area. The results further revealed that the students studying in schools located in environmentally polluted areas have attention, thinking, and decision-making abilities related to cognitive function impairment.

Mechanisms and targeted reversion/prevention of hepatic fibrosis caused by the non-hereditary toxicity of benzo(a)pyrene

The effect of long term exposure to low concentrations of environmental pollutants on hepatic disorders is a major public health concern worldwide. Polycyclic aromatic hydrocarbons (PAHs) are a class of persistent organic pollutants. In recent years, an increasing number of studies have focused on the deleterious effects of low concentrations of PAHs in the initiation or exacerbation of the progression of chronic liver disease. However, the underlying molecular mechanisms and effective intervention methods remain unclear. Here, we found that in hepatocytes, a low concentration of benzo(a)pyrene (B[a]P, an indicator of PAHs) chronic exposure continuously activated 14-3-3η via an epigenetic accumulation of DNA demethylation. As a “switch like” factor, 14-3-3η activated its downstream PI3K/Akt signal, which in turn promoted vascular endothelial growth factor (VEGF) production and secretion. As the characteristic fibrogenic paracrine factor regulated by B[a]P/14-3-3η, VEGF significantly induced the neovascularization and activation of hepatic stellate cells, leading to the development of hepatic fibrosis. Importantly, targeted 14-3-3η by using its specific inhibitor invented by our lab could prevent B[a]P-induced hepatic fibrosis, and could even reverse existent hepatic fibrosis caused by B[a]P. The present study not only revealed novel mechanisms, but also proposed an innovative approach for the targeted reversion/prevention of the harmful effects of exposure to PAHs on chronic liver disease.

Sulfanilamide Electrochemical Sensor Using Phenolic Substrates and CO2 Laser Pyrolysis.

The concentration of environmental pollutants needs to be monitored constantly by reliable analytical methods since they pose a public health risk. Developing simple and affordable sensors for such pollutants can allow for large-scale monitoring economically. Here, we develop a simple electrochemical sensor for sulfanilamide (SFD) quantification using a phenolic resin substrate and a CO2 laser to pyrolyze the sensor geometry over the substrate. The sensors are modified with carbon nanotubes via a simple drop-casting procedure. The carbon nanotube loading effect the electrochemical performance toward a redox probe and analytical performance for SFD detection is investigated, showing no net benefit beyond 1 mg L-1 of carbon nanotubes. The effects of the modification on the SFD oxidation are shown to be more than just an electrode area effect and possibly attributed to the fast electron transfer kinetics of the carbon nanotubes. SFD detection is performed at small solution volumes under static (800 μL) and hydrodynamic conditions (3 mL) in a fully integrated, miniaturized batch-injection analyses cell. Both methods have a similar linear range from 10.0 to 115.0 μmol L-1 and high selectivity for SFD determination. Both systems are used to quantify SFD in real samples as a proof of concept, showcasing the proposed device's applicability as a sensor for environmental and public health monitoring of SFD.

Nano-Structural Superwetting Surfaces for Highly Reliable On-Site Detection of Bisphenol A.

In the domain of big data geographic screening for environmental pollutants, the expeditious dissemination of testing results to environmental investigation professionals is pivotal in facilitating comprehensive analysis and the implementation of more efficacious strategies for managing environmental issues. However, this endeavor can prove to be particularly arduous when conducting examinations in remote, resource-scarce rural areas and field environments, where deficient infrastructure often emerges as the principal impediment to unimpeded environmental monitoring. Therefore, the development of a reliable and portable monitoring strategy with the ability to analyze large amounts of data is highly required. Here, a deep-learning (DL)-assisted portable sensing strategy was developed based on thermal and pH dual-responsive nano-structural superwetting surfaces, for highly reliable, quick, and field monitoring of environmental pollutants. In our experiment, bisphenol A (BPA) was selected as the representative pollute. The achieved limit of detection, attaining a remarkably low value of 1.05 μM, unequivocally adhered to stringent international testing standards for evaluating the migration of BPA in thermal paper. Based on a DL image classification algorithm, highly precise predictions regarding the migration of BPA concentration were achieved, with an accuracy rate exceeding 99%. Furthermore, it successfully facilitated automated and exceedingly reliable monitoring of the migration of BPA from thermal paper within the principal provinces of thermal paper production in China. This strategy engenders the potential to establish correlations between environmental pollutant concentrations in specific regions and the prevalence of certain human ailments.

Performance of Anticorrosive Paint Systems for Carbon Steel in the Antarctic Marine Environment.

This study evaluated the behavior of three paint systems exposed to the Antarctic marine environment for 45 months compared to a control of uncoated carbon steel with a determined corrosion rate. At the study site, all environmental conditions, solar radiation, and the concentration of environmental pollutants (Cl- and SO2) were evaluated. The paint systems differed in terms of the primer and top coat. Coated samples were studied before and after exposure. They were evaluated visually and using SEM to determine adhesion, abrasion, and contact angle; using the Evans X-Cut Tape Test; using ATR-FTIR spectroscopy to analyze the state of aging of the top layer; and using electrochemical impedance spectroscopy (EIS) for coat protection characterization. The corrosion rate obtained for steel was 85.64 µm year-1, which aligned with a C5 environmental corrosivity category. In general, the evaluation in the period studied showed the paint systems had good adhesion and resistance to delamination, without the presence of surface rust, and exhibited some loss of brightness, an increase in the abrasion index, and a decrease in the percentage of reflectance due to aging. EIS showed good protection capability of the three coating schemes. In general, this type of paint system has not previously been evaluated in an extreme environment after 45 months of exposure to the environment. The results showed that the best behavior was found for the system whose top layer was acrylic-aliphatic polyurethane.

Intra- and inter-annual variations in metal concentrations in the superficial water of a highly polluted urban basin of Argentina.

To protect ecosystems impacted by human activities and prevent their degradation, it is imperative to evaluate variations in the concentration of environmental pollutants over time. Here, we evaluated the intra- and inter-annual variations of several metals from 15 sites in the Matanza Riachuelo River basin (one of the most polluted in the world) and determined the physicochemical and meteorological parameters associated with these changes from 2008 to 2015. For this, in each site, we used Asymmetric Eigenvector Maps and Redundancy Analysis. The results highlighted temporal patterns of metal concentrations and several factors associated with them, perhaps related to the actions performed in the area since 2008. Additionally, we found that the effects of physicochemical and meteorological factors on metal concentrations were site-dependent, possibly related to the presence of different local sources of pollution or characteristics of the river in each site, such as its sediments. This approach could be applied to different scenarios (aquatic and terrestrial) and could provide a tool to help decision-makers address the harmful consequences of the continuous advance of human activities on human and ecosystem health.

Evaluation and relation of determinants of risk perception in the resident population living near industrially contaminated sites

The association between industrial pollution and human health is of high importance for public health. Living near industrially contaminated sites (ICSs) and being exposed to increasing concentrations of environmental pollutants along with disadvantaged social and economic conditions result in an increased occurrence of diseases. There are 16 identified industrially contaminated sites in the Republic of North Macedonia, and of all of them, chemical industry AD OHIS - Skopje and lindane dump located near the plant, according to almost all categorizations,has been evaluated to pose the highest level of ecological and health risk, although there has been no recent evidence about these issues. The main aim of this study was to obtain general information about risk perception of resident population living around and near AD OHIS in the Skopje region. Methods: A standardized and modified questionnaire was sent to the participants in an electronic form by e-mail and was published on social networks and municipalities’ web sites. The responses to the questionnaire were given anonymously and voluntarily. The results were analyzed using descriptive statistical methods with calculating central tendency parameters and analytical statistical methods with correlation and Pearson χ2 test and independent sample test. Results: During the observation period, 220 people responded to the survey, with female respondents being predominant (70%). Analysis showed that there was no significant difference between genders regarding risk perception. According to Likert scale, with 95% CI, among anthropogenic sources, respondents stated they were extremely worried about air pollution and very worried or worried about water pollution, noise, waste and dangerous industry. The diseases that trigger a response of greater concern were those related to allergies, respiratory diseases and cancer. Age, education and information related to ecological risks significantly influenced on judgement of the environmental conditions in the living area of resident population (p&lt;0.05). Conclusions: Investigation results showed that resident population in the exposed Skopje region has a high level of perception and is susceptible to ecological risks by anthropogenic and natural influence. There is a direct relationship between exposureand environmental health impact. Also, sociоeconomic characteristics (gender, age, education) and cognitive factors have influence on risk perception level.

INCORPORATING ANN WITH PCR FOR PROGRESSIVE DEVELOPING OF AIR POLLUTION INDEX FORECAST

This study circumscribes the modelling for concentration of Air Pollutant Index (API) in Selangor, Malaysia. The five monitored environmental pollutant concentrations (O3, CO, NO2, SO2, PM10) for ten years (2006 to 2015) data are used in this study to develop the prediction of API. The selected study area is located in rapid urbanised areas and surrounded by a number of industries, and is highly influenced by congested traffic. The principal component regression (PCR) for the combination of the principal component analysis together with multiple regression analysis, and artificial neural network (ANN), are used to predict the API concentration level. An additional approach using a combination method of PCR and ANN are included into the study to improve the API accuracy of prediction. The resulting prediction models are consistent with the observed value. The prediction techniques of PCR, ANN, and a combination method of R2 values are 0.931, 0.956, and 0.991 respectively. The combination method of PCR and ANN are detected to reduce the root mean square error (RMSE) of API concentration. In conclusion, different techniques were used in the combination method of API prediction which had improved and provided better accuracy rather than being dependent on the single prediction model.

Uptake and Transport of Different Concentrations of PPCPs by Vegetables.

In many parts of the world, water resources are scarce or even extremely scarce, and the reuse of water resources has become mainstream in today's world. Many regions use treated wastewater for agricultural irrigation, aquaculture, and other activities. However, in recent years, wastewater has been found to contain large amounts of pharmaceuticals and personal care products (PPCPs). Therefore, there is a potential risk of PPCPs being transported in the environment and affecting human health. In this study, we compared the uptake, transport, and accumulation of 27 PPCPs in three types of sprouts (radish, buckwheat, and okra).The bioaccumulation of amantadine, diphenhydramine, chlorpheniramine maleate, sibutramine, hemosibutramine, chlorosibutramine, N-monomethyl sibutramine, N, N-desmethyl sibutramine, and carbamazepine was found to be significantly higher in plants grown for 12 days in media containing 0.5, 5.0, and 50.0 ng/mL PPCPs. With increasing concentration of PPCPs in the culture solution, the amount of PPCPs absorbed by plants and the degree of accumulation also showed an increasing trend. At the same time, it was demonstrated that there was an obvious uptake transfer phenomenon of PPCPs by plants, and the trend of uptake transfer became more and more obvious as the concentration of external environmental pollutants increased. In addition, amantadine, chlorpheniramine maleate, carbamazepine, N, N-desmethyl sibutramine, hemosibutramine, and chlorosibutramine showed more active translocation in some plants (TF > 1.0).

Pet cats, the better sentinels for indoor organic pollutants

Pets are the most intimate companions of humans, as pets and people share most of their lives indoors. Based on the connections between pet animals, humans, and the environment, pet cats and dogs are often recommended as sentinels for the detection of environmental contaminants and for comorbidity tracking. However, their suitability as sentinels is yet to be established. Persistent organic pollutants and environmental hormones have replaced particulate matter (PM2.5) and volatile organic compounds (VOCs) in causing indoor air pollution. This review summarises the differences in the types and concentrations of indoor organic environmental pollutants detected in pet dogs and cats. This includes an analysis of the main exposure routes of different types of pollutants. To identify which of the two pet species are better sentinels, cats and dogs were compared based on their metabolic capacities of various indoor organic pollutants. In addition to PM2.5, a range of organic compounds including polychlorinated biphenyls, polybrominated diphenyl ethers (PBDEs), bromophenols, perfluoroalkyl substances, organochlorine pesticides, fungicides, polycyclic aromatic hydrocarbons, phthalic acid esters, organophosphate pesticides, organophosphorus flame retardants, and melamine have been detected in both dogs and cats. Pets often accumulate PBDEs from dust; however, traces of PBDEs are present in their diet. The indoor pollutant contamination levels in internal cats were generally higher than those in dogs. Cats accumulate organic pollutants associated with indoor environments, but they are sensitive to their toxicity because, unlike dogs, cats cannot metabolise most of the accumulated pollutants. Moreover, cats share similar clinical symptoms of thyroid diseases in humans. Based on the above observations of detection of indoor organic pollutants, it could be said that cats are better sentinels than dogs.

Phthalate acid esters and polycyclic aromatic hydrocarbons concentrations with their determining factors among Chinese pregnant women: A focus on dietary patterns

BackgroundPregnant women are susceptible to adverse health effects associated with phthalate acid esters (PAEs) and polycyclic aromatic hydrocarbons (PAHs), and diet is a significant exposure source. Little is known about the contributions of dietary patterns during pregnancy to the exposure variability of these environmental contaminants. ObjectivesTo identify dietary patterns in relation to PAEs and PAHs exposure in the Chinese pregnant population. MethodsDietary data and urinary concentrations of environmental pollutants were obtained from 1190 pregnant women in the Tongji Birth Cohort (TJBC). PAEs and PAHs were measured in spot urine samples. Food intake was assessed using a food-frequency questionnaire. Dietary patterns were constructed by principal component analysis (PCA). Through PCA, we also extracted three chemical mixture scores that represent different co-exposure patterns of PAEs and PAHs. Multiple linear regression models were adopted to identify predictors of PAEs and PAHs exposure. ResultsFour dietary patterns were identified by PCA that explained 44.9 % of the total variance of food intake. We found egg-dairy products pattern, whole grain-tuber crop pattern, and meat-aquatic products pattern were positively associated with specific pollutants exposure. In contrast, fruit-nut-vegetable pattern was negatively correlated with PAEs and PAHs exposure. Every SD increase in this pattern score was associated with 14.36 % reduced mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) (95 % CI: −24.50 ~ −2.96, p-trend = 0.01), 10.86 % reduced 2-hydroxynaphthalene (2-OHNap) (95 % CI: −20.07 ~ −0.60, p-trend = 0.04), 19.35 % reduced 9-hydroxyphenanthrene (9-OHPhe) (95 % CI: −34.49 ~ −0.70, p-trend = 0.01), and 8.33 % reduced scores of PAHs group (95 % CI: −15.97 ~ −0.10, p-trend = 0.02). In addition, disposable tableware usage and passive smoking were suggested as potentially modifiable sources of PAEs and PAHs exposure, respectively. ConclusionAdhering to egg-dairy products pattern, whole grain-tuber crop pattern, and meat-aquatic products pattern may be related to increased PAEs and PAHs exposure, while following fruit-nut-vegetable pattern seems to correlate with a lower burden of such exposure.

Effect of environmental factors on blood counts of Gambusia affinis caught at Brantas River watershed, Indonesia.

Background: Contamination of freshwater ecosystems has become a major issue as it threatens public water sources as well as aquatic life. It is important to predict changes in organism health, given a known number of environmental factors and pollutant concentrations, in order to better manage contaminants through biomarker analysis. This study aims to examine the ecosystem health of the Brantas River based on its environmental condition and the hematology profile of Gambusia affinis fish present in the river. This species was chosen because of its wide distribution along the Brantas River, and because it is very tolerant, adaptable, highly abundant, and easy to catch. Methods: The study area included 10 sampling sites along the Brantas River watershed. In total, six water quality parameters were observed (temperature, pH, dissolved oxygen (DO), biological oxygen demand (BOD), ammonia concentration, and phenol concentration) and hematology measurements consisted of erythrocyte, leucocyte, and micronuclei analyses. Results: The results showed that the upstream area of Brantas River, located in Batu, was the least polluted region, while Mojokerto was the most polluted. The erythrocyte level of Gambusia affinis caught in most sampling sites was quite low. Furthermore, research revealed that the status of Gambusia affinis' hematological profile was significantly correlated (p<0.05) with water quality parameters, particularly DO, BOD, ammonia, and phenol. Conclusions: It can be concluded from these results that the hematological profile of the fish is poor due to high levels of organic waste and harmful substances.